Mobile devices with various methods of connectivity are now for many people becoming the primary gateway to the internet and also a major storage point for personal information. This is in addition to the normal range of personal computers and furthermore sensor devices plus internet based providers. Combining these devices together and lately the applications and the information stored by those applications is a major challenge of interoperability. This can be achieved through numerous, individual and personal information spaces in which persons, groups of persons, etc. can place, share, interact and manipulate webs of information with their own locally agreed semantics without necessarily conforming to an unobtainable, global whole.
Furthermore, in addition to information, the information spaces may be combined with webs of shared and interactive computations or computation spaces consisting of a multitude of devices so that each device, as parts of the computation spaces, can have the information in the information space manipulated within the computation space environment, which may include devices other than the device, and the results delivered to the device, rather than the whole process being performed locally in the device. These combined information spaces and computation spaces often referred to as computation clouds, are extensions of the ‘Giant Global Graph’ in which one can apply semantics and reasoning at a local level.
In one example, clouds are working spaces respectively embedded with distributed information and computation infrastructures spanned around computers, information appliances, processing devices and sensors that allow people to work efficiently through access to information and computations from computers or other devices. An information space or a computation space can be rendered by the computation devices physically presented as heterogeneous networks (wired and wireless). However, despite the fact that information and computation presented by the respective spaces can be distributed with different granularity, still in certain example implementations achieving scalable high context information processing within such heterogeneous environments is a challenging task.
For example, in various embodiments, a computation cloud can provide and/or recommend various services to a device user, by analyzing user information (e.g., using analytics, algorithms, etc.) However, the device may only receive the benefits of cloud services and recommendations when it directly interacts with the web services.
In one embodiment, under some circumstances, there may be no possibility for a device to connect to a cloud. For example, congestion, limited signaling capabilities (lack of network) or even cost issues (data charges when roaming) may prohibit the ability to form a live connection with the cloud. However, the user data within the device is potentially rich with insight into consumer behavior. If the user data can be distributed (propagated) to the cloud, at the time of connection, it can be mined and analyzed at the cloud for future insights which enable preemptive recommendations to the device user based on future events.